Time-aware Sub-Trajectory Clustering in Hermes@PostgreSQL

In this paper, we present an efficient in-DBMS framework for progressive time-aware sub-trajectory cluster analysis. In particular, we address two variants of the problem: (a) spatiotemporal sub-trajectory clustering and (b) index-based time-aware clustering at querying environment. Our approach for (a) relies on a two-phase process: a voting-and-segmentation phase followed by a sampling-and-clustering phase. Regarding (b), we organize data into partitions that correspond to groups of sub-trajectories, which are incrementally maintained in a hierarchical structure. Both approaches have been implemented in Hermes@PostgreSQL, a real Moving Object Database engine built on top of PostgreSQL, enabling users to perform progressive cluster analysis via simple SQL. The framework is also extended with a Visual Analytics (VA) tool to facilitate real world analysis

Trajectory collection and reconstruction

The research area of trajectory databases has addressed the need for representing movements of objects (i.e., trajectories) in databases in order to perform ad hoc querying and analysis on them. During the last decade, there has been a lot of research ranging from data models and query languages to implementation aspects, such as efficient indexing, query processing, and optimization techniques. This chapter covers aspects related to data collection and handling so as to feed trajectory databases with appropriate data. We will also focus on the step trajectory reconstruction of the Geographic Privacy-aware KDD process (illustrated in Figure 2.1) emerged from the GeoPKDD project which proposed some solid theoretical foundations at an appropriate level of abstraction to deal with traces and trajectories of moving objects aiming at serving real world applications. This process consists of a set of techniques and methodologies that are applicable to mobility data and are organized in some well-defined and individual steps that have a clear target: to extract user-consumable forms of knowledge from large amounts of raw geographic data referenced in space and in time. However, when mobility data are about individuals, data collection is subject to privacy regulations and restrictions. To enable privacy-aware collection of position data, a complementary class of techniques is used, known as location PETs (privacy-enhancing technologies). This KDD process can be applied to heterogeneous sources of mobility data

Compact Trip Representation over Networks

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-46049-9_23[Abstract] We present a new Compact Trip Representation ( CTRCTR ) that allows us to manage users’ trips (moving objects) over networks. These could be public transportation networks (buses, subway, trains, and so on) where nodes are stations or stops, or road networks where nodes are intersections. CTRCTR represents the sequences of nodes and time instants in users’ trips. The spatial component is handled with a data structure based on the well-known Compressed Suffix Array ( CSACSA ), which provides both a compact representation and interesting indexing capabilities. We also represent the temporal component of the trips, that is, the time instants when users visit nodes in their trips. We create a sequence with these time instants, which are then self-indexed with a balanced Wavelet Matrix ( WMWM ). This gives us the ability to solve range-interval queries efficiently. We show how CTRCTR can solve relevant spatial and spatio-temporal queries over large sets of trajectories. Finally, we also provide experimental results to show the space requirements and query efficiency of CTRCTR .Ministerio de Economía y Competitividad; TIN2013-46238-C4-3-RMinisterio de Economía y Competitividad; TIN2013-47090-C3-3-PMinisterio de Economía y Competitividad; IDI-20141259Ministerio de Economía y Competitividad; ITC-20151305Ministerio de Economía y Competitividad; ITC-20151247Xunta de Galicia; GRC2013/053Chile.Fondo Nacional de Desarrollo Científico y Tecnológico; 1140428Chile. Instituto de Sistemas Complejos de Ingeniería ; FBO 1

Increasing maritime situation awareness via trajectory detection, enrichment and recognition of events

The research presented in this paper aims to show the deployment and use of advanced technologies towards processing surveillance data for the detection of events, contributing to maritime situation awareness via trajectories’ detection, synopses generation and semantic enrichment of trajectories. We first introduce the context of the maritime domain and then the main principles of the big data architecture developed so far within the European funded H2020 datAcron project. From the integration of large maritime trajectory datasets, to the generation of synopses and the detection of events, the main functions of the datAcron architecture are developed and discussed. The potential for detection and forecasting of complex events at sea is illustrated by preliminary experimental results

Big data analytics for time critical maritime and aerial mobility forecasting

The correlated exploitation of heterogeneous data sources offering very large archival and streaming data is important to increase the accuracy of computations when analysing and predicting future states of moving entities. Aiming to significantly advance the capacities of systems to improve safety and effectiveness of critical operations involving a large number of moving entities in large geographical areas, this paper describes progress achieved towards time critical big data analytics solutions to user-defined challenges in the air-traffic management and maritime domains. Besides, this paper presents further research challenges concerning data integration and management, predictive analytics for trajectory and events forecasting, and visual analytics

Engineering reconnaissance following the August 24, 2016 M6.0 Central Italy earthquake

An earthquake with a moment magnitude reported as 6.0 from INGV (Istituto Nazionale di Geofisica e Vulcanologia); occurred at 03:36 AM (local time) on 24 August 2016 in the central part of Italy. The epicenter was located at the borders of the Lazio, Abruzzi, Marche and Umbria regions, about 2.5 km north-east of the village of Accumoli and about 100 km from Rome. The hypocentral depth was about 8 km (INGV). We summarize preliminary findings of the Italy-US GEER (Geotechnical Extreme Events Reconnaissance) team, on damage distribution, causative faults, earthquake-induced landslides and rockfalls, building and bridge performance, and ground motion characterization. Our reconnaissance team used multidisciplinary approaches, combining expertise in geology, seismology, geomatics, geotechnical engineering, and structural engineering. Our approach was to combine traditional reconnaissance activities of on-ground recording and mapping of field conditions, with advanced imaging and damage detection routines enabled by state-of-the-art geomatics technology. We anticipate that results from this study, will be useful for future post-earthquake reconnaissance efforts, and improved emergency respons

Fuzzy clustering of intuitionistic fuzzy data

Challenged by real-world clustering problems this paper proposes a novel fuzzy clustering scheme of datasets produced in the context of intuitionistic fuzzy set theory. More specifically, we introduce a variant of the Fuzzy C-Means (FCM) clustering algorithm that copes with uncertainty and a similarity measure between intuitionistic fuzzy sets, which is appropriately integrated in the clustering algorithm. We describe an intuitionistic fuzzification of colour digital images upon which we applied the proposed scheme. The experimental evaluation of the proposed scheme shows that it can be more efficient and more effective than the well-established FCM algorithm, opening perspectives for various applications. © 2008, Inderscience Publishers